Title :
Convergence dynamics as an indicator for progressive addition lens acceptability among presbyopes
Author :
Han, Sang Jin ; Semmlow, John L. ; Granger-Donnetti, Bérangère ; Alvarez, Tara L.
Author_Institution :
Depart. of Biomed. Eng., New Jersey Inst. of Technol., Newark, NJ
Abstract :
Presbyopia is a universal vision problem caused by the aging process. Progressive addition lenses (PALs) are a preferred solution; however, some patients can not adapt to the lenses. The acceptability is not well understood. Sixteen presbyopic subjects (9 subjects adapted to progressive lenses (PL) and 7 subjects who could not adapt to progressive lenses (PD) ) participated in a motor learning study. The motor learning experiment recorded baseline and modification responses where the dynamics of the 4deg step responses were compared. Results show that both baseline and modified convergence dynamics were significantly greater in PLs compared to PDs. An adaptive neural network (ANN) classification technique using the baseline and modification velocity parameters with a linear classifier resulted in 94% correct classification. Therefore, baseline 4deg convergence dynamics and its modification rate may be used as indicators to predict progressive addition lens acceptability among presbyopes.
Keywords :
neural nets; neurophysiology; ophthalmic lenses; vision defects; adaptive neural network classification technique; aging process; convergence dynamics; linear classifier; modification velocity parameters; modified convergence dynamics; motor learning; presbyopes; progressive addition lens acceptability; vision problem; Adaptive systems; Aging; Artificial neural networks; Biomedical engineering; Convergence; Humans; Lenses; Neural networks; Surgery; Vision defects;
Conference_Titel :
Bioengineering Conference, 2009 IEEE 35th Annual Northeast
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-4362-8
Electronic_ISBN :
978-1-4244-4364-2
DOI :
10.1109/NEBC.2009.4967785